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Updated: May 10, 2026

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor
Published on: May 7, 2021
ICF-PR-Net: a deep phase retrieval neural network for X-ray phase contrast imaging of inertial confinement fusion
Abstract:
X-ray phase contrast imaging (XPCI) has demonstrated capability to characterize inertial confinement fusion (ICF) capsules, and phase retrieval can reconstruct phase information from intensity images. This study introduces ICF-PR-Net, a novel deep learning-based phase retrieval method for ICF-XPCI. We numerically constructed datasets based on ICF capsule shape features, and proposed an object-image loss function to add image formation physics to network training. ICF-PR-Net outperformed traditional methods as it exhibited satisfactory robustness against strong noise and nonuniform background and was well-suited for ICF-XPCI's constrained experimental conditions and single exposure limit. Numerical and experimental results showed that ICF-PR-Net accurately retrieved the phase and absorption while maintaining retrieval quality in different situations. Overall, the ICF-PR-Net enables the diagnosis of the inner interface and electron density of capsules to address ignition-preventing problems, such as hydrodynamic instability growth.
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